Polymarket vs Kalshi data: how prediction market datasets differ

Polymarket and Kalshi both surface event probabilities, but their market structures, rule systems, regulatory context, and API objects differ. A dataset that looks like odds on both venues may have different settlement mechanics underneath.

For analysts, the key is to normalize carefully. Probability, volume, liquidity, expiry, source text, and status should be mapped explicitly rather than assumed equivalent.

A scanner that works for one venue may over-rank or under-rank markets on another if liquidity, volume, or expiry fields are defined differently. Likewise, a resolution-risk model depends on each venue's rules and finality process.

This is why cross-venue analytics should show the source venue, fetched time, and derived assumptions near every result.

When comparing market data providers, evaluate the practical fields first: market ID, title, outcomes, price, spread, volume, liquidity, orderbook depth, end date, status, resolution text, and official source.

Then evaluate workflow fields: can you search, subscribe, backtest, cite, and alert on the data without scraping rendered pages?

• Market identity: slug, ticker, condition ID, or exchange contract ID.
• Price semantics: outcome probability, bid/ask, midpoint, or last trade.
• Resolution: rule text, source, oracle or exchange finality.
• Freshness: fetched_at, cache_seconds, and valid-until.

Normalize to a common research object, but keep venue-specific fields visible. Do not hide the source rule just because both markets appear to ask the same question.

Before publishing cross-venue claims, verify each venue's current official docs and cite the timestamp of your data pull.